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All issues Volume 61 (2019) SHS Web Conf., 61 (2019) 01006 References
Open Access
Issue
SHS Web Conf.
Volume 61, 2019
Innovative Economic Symposium 2018 - Milestones and Trends of World Economy (IES2018)
Article Number 01006
Number of page(s) 13
Section Strategic Partnerships in International Trade
DOI https://doi.org/10.1051/shsconf/20196101006
Published online 30 January 2019
  1. M. Sheikhan, N. Mohammadi, Time series prediction using PSO-optimized neural network and hybrid feature selection algorithm for IEEE load data: revue littéraire mensuelle. Neural Computing and Applications, 23(3-4), 1185-1194, (2013) [CrossRef] [Google Scholar]
  2. S. De Baets, N. Harvey, Forecasting from time series subject to sporadic perturbations:Effectiveness of different types of forecasting support. International Journal of Forecasting, 34(2), 163-180, (2018) [CrossRef] [Google Scholar]
  3. J. Jaramillo, J. D. Velasquez, C. J. Franco, Research in Financial Time Series Forecasting with SVM: Contributions from Literature. IEEE Latin America Transactions, 15(1), 145-153, (2017) [CrossRef] [Google Scholar]
  4. P. Rostan, A. Rostan, The versatility of spectrum analysis for forecasting financial time series. Journal of Forecasting, 37(3), 327-339, (2018) [CrossRef] [Google Scholar]
  5. S. Hašková, Prediction of Future Development of Share Prices Using Neural Networks Based on Daily and Monthly Share Price Data. Mladá veda, 5(9), 33-48, (2017) [Google Scholar]
  6. Z. Qun, L. Xu, G. Zhang, LSTM Neural Network with Emotional Analysis for Prediction of Stock Price. Engineering Letters, 25(2), 167-175, (2017) [Google Scholar]
  7. M. Vochozka, Inventory management using artificial neural networks in a concrete case. Proceedings of the 5th International Conference Innovation Management, Entrepreneurship and Sustainability, Prague, Czech Republic, 1084-1094, (2017) [Google Scholar]
  8. M. Ghiassi, H. Saidane, D. K. Zimbra, A dynamic artificial neural network model for forecasting time series events. International Journal of Forecasting, 21(2), 341-362, (2005) [CrossRef] [Google Scholar]
  9. X. Zhang, Evolution of ARMA demand in supply chains. Manufacturing and Service Operations Management, 6(2), 195-198, (2004) [CrossRef] [Google Scholar]
  10. J. Vrbka, Z. Rowland, Stock price development forecasting using neural networks. Proceedings of the International conference Innovative Economic Symposium 2017, Strategic Partnerships in International Trade, Web of Conferences, 39, (2017) [Google Scholar]
  11. Y. H. Hu, J. Hwang, Handbook of neural network signal processing. Boca Raton: CRC Press, (2002) [Google Scholar]
  12. Y. Chen, B. Yang, J. Dong, Time-series prediction using a local linear wavelet neural network. Neurocomputing, 69(4-6), 449-465, (2006) [CrossRef] [Google Scholar]
  13. D. Santin, On the approximation of production functions: a comparison of artificial neural networks frontiers and efficiency techniques. Applied Economics Letters, 15(8), 597-600, (2008) [CrossRef] [Google Scholar]
  14. P. Šuleř, Using Kohonen’s neural networks to identify the bankruptcy of enterprises:Case study based on construction companies in South Bohemian region. Proceedings of the 5th International Conference Innovation Management, Entrepreneurship and Sustainability, Prague, Czech Republic, 985-995, (2017) [Google Scholar]
  15. M. Vochozka, Formation of complex company evaluation method through neural networks based on the example of construction companies collection. AD ALTA:Journal of Interdisciplinary Research, 7(2), 232-239, (2018) [Google Scholar]
  16. P. H. B. F. Franses, The Econometric Modelling of Financial Time Series. International Journal of Forecasting, 16(3), 426-427, (2000) [CrossRef] [Google Scholar]
  17. A. B. Sánchez, C. Ordóñez, F. S. Lasheras, F. J. De Cos Juez, J. Roca-Pardiñas, Forecasting SO 2 Pollution Incidents by means of Elman Artificial Neural Networks and ARIMA Models. Abstract and Applied Analysis, 1-6, (2013) [CrossRef] [Google Scholar]
  18. G. Mélard, J. M. Pasteels, Automatic ARIMA modeling including interventions, using time series expert software. International Journal of Forecasting, 16(4), 497-508, (2000) [CrossRef] [Google Scholar]
  19. M. F. Anaghi, Y. Norouzi, A Model for Stock Price Forecasting Based on ARMA Systems. 2nd International Conference on Advances in Computational Tools for Engineering Applications, Beirut, Lebanon, 265-268, (2012) [Google Scholar]
  20. L. Marek, M. Vrabec, ARIMA models and exponential smoothing. 33rd International Conference on Mathematical Methods in Economics, Czech Republic, 502-507, (2015) [Google Scholar]
  21. B. Billah, M. L. King, R. D. Snyder, A. B. Koehler, Exponential smoothing model selection for forecasting. International Journal of Forecasting, 22(2), 239-247, (2006) [CrossRef] [Google Scholar]
  22. Unipetrol, Unipetrol company in brief [online], Available at:http://www.unipetrol.cz/cs/VztahySInvestory/Stranky/unipetrol-ve-zkratce.aspx, (2018) [Google Scholar]

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